
"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFLEDForConditionalGeneration, TFLEDModel



@require_tf
class 	__a	:
       UpperCamelCase_		:       int				=      LEDConfig
       UpperCamelCase_		:       Optional[int]				=      {}
       UpperCamelCase_		:       Optional[int]				=      '''gelu'''






       def __init__(	self			:				Any							,   UpperCAmelCase_			:				Any							,   UpperCAmelCase_			:				Any=13							,   UpperCAmelCase_			:				int=7							,   UpperCAmelCase_			:				Dict=True							,   UpperCAmelCase_			:				int=False							,   UpperCAmelCase_			:				List[str]=99							,   UpperCAmelCase_			:				List[str]=32							,   UpperCAmelCase_			:				List[Any]=2							,   UpperCAmelCase_			:				Union[str, Any]=4							,   UpperCAmelCase_			:				int=37							,   UpperCAmelCase_			:				List[Any]=0.1							,   UpperCAmelCase_			:				Union[str, Any]=0.1							,   UpperCAmelCase_			:				str=20							,   UpperCAmelCase_			:				List[Any]=2							,   UpperCAmelCase_			:				Dict=1							,   UpperCAmelCase_			:				str=0							,   UpperCAmelCase_			:				Any=4							,   )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		parent
              UpperCamelCase       			=		batch_size
              UpperCamelCase       			=		seq_length
              UpperCamelCase       			=		is_training
              UpperCamelCase       			=		use_labels
              UpperCamelCase       			=		vocab_size
              UpperCamelCase       			=		hidden_size
              UpperCamelCase       			=		num_hidden_layers
              UpperCamelCase       			=		num_attention_heads
              UpperCamelCase       			=		intermediate_size

              UpperCamelCase       			=		hidden_dropout_prob
              UpperCamelCase       			=		attention_probs_dropout_prob
              UpperCamelCase       			=		max_position_embeddings
              UpperCamelCase       			=		eos_token_id
              UpperCamelCase       			=		pad_token_id
              UpperCamelCase       			=		bos_token_id
              UpperCamelCase       			=		attention_window

              # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
              # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
              # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
              # because its local attention only attends to `self.attention_window` and one before and one after
              UpperCamelCase       			=		self.attention_window + 2

              # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
              # the `test_attention_outputs` and `test_hidden_states_output` tests
              UpperCamelCase       			=		(
                  self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
              )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		ids_tensor([self.batch_size, self.seq_length - 1]							,   self.vocab_size      )
              UpperCamelCase       			=		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )							,   1      )
              UpperCamelCase       			=		tf.concat([input_ids, eos_tensor]							,   axis=1      )

              UpperCamelCase       			=		ids_tensor([self.batch_size, self.seq_length]							,   self.vocab_size      )

              UpperCamelCase       			=		self.config_cls(
                  vocab_size=self.vocab_size							,   d_model=self.hidden_size							,   encoder_layers=self.num_hidden_layers							,   decoder_layers=self.num_hidden_layers							,   encoder_attention_heads=self.num_attention_heads							,   decoder_attention_heads=self.num_attention_heads							,   encoder_ffn_dim=self.intermediate_size							,   decoder_ffn_dim=self.intermediate_size							,   dropout=self.hidden_dropout_prob							,   attention_dropout=self.attention_probs_dropout_prob							,   max_position_embeddings=self.max_position_embeddings							,   eos_token_ids=[2]							,   bos_token_id=self.bos_token_id							,   pad_token_id=self.pad_token_id							,   decoder_start_token_id=self.pad_token_id							,   attention_window=self.attention_window							,   **self.config_updates							,   )
              UpperCamelCase       			=		prepare_led_inputs_dict(UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )
              UpperCamelCase       			=		tf.concat(
                  [tf.zeros_like(UpperCAmelCase_      )[:, :-1], tf.ones_like(UpperCAmelCase_      )[:, -1:]]							,   axis=-1							,   )
              UpperCamelCase       			=		global_attention_mask
              return config, inputs_dict






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any							,   UpperCAmelCase_			:				List[Any]							,   UpperCAmelCase_			:				Tuple      )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		TFLEDModel(config=UpperCAmelCase_      ).get_decoder()
              UpperCamelCase       			=		inputs_dict["input_ids"]

              UpperCamelCase       			=		input_ids[:1, :]
              UpperCamelCase       			=		inputs_dict["attention_mask"][:1, :]
              UpperCamelCase       			=		1

              # first forward pass
              UpperCamelCase       			=		model(UpperCAmelCase_							,   attention_mask=UpperCAmelCase_							,   use_cache=UpperCAmelCase_      )

              UpperCamelCase			,							UpperCamelCase       			=		outputs.to_tuple()

              # create hypothetical next token and extent to next_input_ids
              UpperCamelCase       			=		ids_tensor((self.batch_size, 3)							,   config.vocab_size      )
              UpperCamelCase       			=		tf.cast(ids_tensor((self.batch_size, 3)							,   2      )							,   tf.inta      )

              # append to next input_ids and
              UpperCamelCase       			=		tf.concat([input_ids, next_tokens]							,   axis=-1      )
              UpperCamelCase       			=		tf.concat([attention_mask, next_attn_mask]							,   axis=-1      )

              UpperCamelCase       			=		model(UpperCAmelCase_							,   attention_mask=UpperCAmelCase_      )[0]
              UpperCamelCase       			=		model(UpperCAmelCase_							,   attention_mask=UpperCAmelCase_							,   past_key_values=UpperCAmelCase_      )[0]

              self.parent.assertEqual(next_tokens.shape[1]							,   output_from_past.shape[1]      )

              # select random slice
              UpperCamelCase       			=		int(ids_tensor((1,)							,   output_from_past.shape[-1]      )      )
              UpperCamelCase       			=		output_from_no_past[:, -3:, random_slice_idx]
              UpperCamelCase       			=		output_from_past[:, :, random_slice_idx]

              # test that outputs are equal for slice
              tf.debugging.assert_near(UpperCAmelCase_							,   UpperCAmelCase_							,   rtol=1e-3      )





def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_=None		,   UpperCAmelCase_=None		,   UpperCAmelCase_=None		,   UpperCAmelCase_=None		,   )->				Optional[int]:







       """simple docstring"""
       if attention_mask is None:
              UpperCamelCase       			=		tf.cast(tf.math.not_equal(UpperCAmelCase_		,   config.pad_token_id					)		,   tf.inta					)
       if decoder_attention_mask is None:
              UpperCamelCase       			=		tf.concat(
                  [
                      tf.ones(decoder_input_ids[:, :1].shape		,   dtype=tf.inta					),
                      tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]		,   config.pad_token_id					)		,   tf.inta					),
                  ]		,   axis=-1		,   )
       if head_mask is None:
              UpperCamelCase       			=		tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
       if decoder_head_mask is None:
              UpperCamelCase       			=		tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
       return {
           "input_ids": input_ids,
           "attention_mask": attention_mask,
           "decoder_input_ids": decoder_input_ids,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
       }



@require_tf
class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       Optional[Any]				=      (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
       UpperCamelCase_		:       Tuple				=      (TFLEDForConditionalGeneration,) if is_tf_available() else ()
       UpperCamelCase_		:       Optional[Any]				=      (
           {
               '''conversational''': TFLEDForConditionalGeneration,
               '''feature-extraction''': TFLEDModel,
               '''summarization''': TFLEDForConditionalGeneration,
               '''text2text-generation''': TFLEDForConditionalGeneration,
               '''translation''': TFLEDForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       UpperCamelCase_		:       Any				=      True
       UpperCamelCase_		:       List[str]				=      False
       UpperCamelCase_		:       Tuple				=      False
       UpperCamelCase_		:       Optional[Any]				=      False






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	str:







              """simple docstring"""
              UpperCamelCase       			=		TFLEDModelTester(self      )
              UpperCamelCase       			=		ConfigTester(self							,   config_class=UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	int:







              """simple docstring"""
              self.config_tester.run_common_tests()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	Union[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs_for_common()
              self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Dict:







              """simple docstring"""
              UpperCamelCase			,							UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs_for_common()
              UpperCamelCase       			=		tf.zeros_like(inputs_dict["attention_mask"]      )
              UpperCamelCase       			=		2
              UpperCamelCase       			=		tf.where(
                  tf.range(self.model_tester.seq_length      )[None, :] < num_global_attn_indices							,   1							,   inputs_dict["global_attention_mask"]							,   )

              UpperCamelCase       			=		True
              UpperCamelCase       			=		self.model_tester.seq_length
              UpperCamelCase       			=		self.model_tester.encoder_seq_length

              def check_decoder_attentions_output(UpperCAmelCase_			:				Optional[Any]      ):
                     UpperCamelCase       			=		outputs.decoder_attentions
                     self.assertEqual(len(UpperCAmelCase_      )							,   self.model_tester.num_hidden_layers      )
                     self.assertListEqual(
                         list(decoder_attentions[0].shape[-3:]      )							,   [self.model_tester.num_attention_heads, seq_length, seq_length]							,   )

              def check_encoder_attentions_output(UpperCAmelCase_			:				Union[str, Any]      ):
                     UpperCamelCase       			=		[t.numpy() for t in outputs.encoder_attentions]
                     UpperCamelCase       			=		[t.numpy() for t in outputs.encoder_global_attentions]
                     self.assertEqual(len(UpperCAmelCase_      )							,   self.model_tester.num_hidden_layers      )
                     self.assertEqual(len(UpperCAmelCase_      )							,   self.model_tester.num_hidden_layers      )
                     self.assertListEqual(
                         list(attentions[0].shape[-3:]      )							,   [self.model_tester.num_attention_heads, seq_length, seq_length]							,   )
                     self.assertListEqual(
                         list(global_attentions[0].shape[-3:]      )							,   [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]							,   )

              for model_class in self.all_model_classes:
                     UpperCamelCase       			=		True
                     UpperCamelCase       			=		False
                     UpperCamelCase       			=		False
                     UpperCamelCase       			=		model_class(UpperCAmelCase_      )
                     UpperCamelCase       			=		model(self._prepare_for_class(UpperCAmelCase_							,   UpperCAmelCase_      )      )
                     UpperCamelCase       			=		len(UpperCAmelCase_      )
                     self.assertEqual(config.output_hidden_states							,   UpperCAmelCase_      )
                     check_encoder_attentions_output(UpperCAmelCase_      )

                     if self.is_encoder_decoder:
                            UpperCamelCase       			=		model_class(UpperCAmelCase_      )
                            UpperCamelCase       			=		model(self._prepare_for_class(UpperCAmelCase_							,   UpperCAmelCase_      )      )
                            self.assertEqual(config.output_hidden_states							,   UpperCAmelCase_      )
                            check_decoder_attentions_output(UpperCAmelCase_      )

                     # Check that output attentions can also be changed via the config
                     del inputs_dict["output_attentions"]
                     UpperCamelCase       			=		True
                     UpperCamelCase       			=		model_class(UpperCAmelCase_      )
                     UpperCamelCase       			=		model(self._prepare_for_class(UpperCAmelCase_							,   UpperCAmelCase_      )      )
                     self.assertEqual(config.output_hidden_states							,   UpperCAmelCase_      )
                     check_encoder_attentions_output(UpperCAmelCase_      )

                     # Check attention is always last and order is fine
                     UpperCamelCase       			=		True
                     UpperCamelCase       			=		True
                     UpperCamelCase       			=		model_class(UpperCAmelCase_      )
                     UpperCamelCase       			=		model(self._prepare_for_class(UpperCAmelCase_							,   UpperCAmelCase_      )      )

                     self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)							,   len(UpperCAmelCase_      )      )
                     self.assertEqual(model.config.output_hidden_states							,   UpperCAmelCase_      )
                     check_encoder_attentions_output(UpperCAmelCase_      )






       @unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."      )
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]      )->	str:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Optional[Any]:







              """simple docstring"""
              # TODO: Head-masking not yet implement
              pass





def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Tuple:







       """simple docstring"""
       return tf.constant(UpperCAmelCase_		,   dtype=tf.intaa					)


SCREAMING_SNAKE_CASE			  =  1E-4



@slow
@require_tf
class 	__a	(   unittest.TestCase						):






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"      ).led

              # change to intended input here
              UpperCamelCase       			=		_long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]      )
              UpperCamelCase       			=		_long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]      )
              UpperCamelCase       			=		prepare_led_inputs_dict(model.config							,   UpperCAmelCase_							,   UpperCAmelCase_      )
              UpperCamelCase       			=		model(**UpperCAmelCase_      )[0]
              UpperCamelCase       			=		(1, 1_024, 768)
              self.assertEqual(output.shape							,   UpperCAmelCase_      )
              # change to expected output here
              UpperCamelCase       			=		tf.convert_to_tensor(
                  [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]							,   )
              tf.debugging.assert_near(output[:, :3, :3]							,   UpperCAmelCase_							,   atol=1e-3      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"      )

              # change to intended input here
              UpperCamelCase       			=		_long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]      )
              UpperCamelCase       			=		_long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]      )
              UpperCamelCase       			=		prepare_led_inputs_dict(model.config							,   UpperCAmelCase_							,   UpperCAmelCase_      )
              UpperCamelCase       			=		model(**UpperCAmelCase_      )[0]
              UpperCamelCase       			=		(1, 1_024, model.config.vocab_size)
              self.assertEqual(output.shape							,   UpperCAmelCase_      )
              # change to expected output here
              UpperCamelCase       			=		tf.convert_to_tensor(
                  [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]							,   )
              tf.debugging.assert_near(output[:, :3, :3]							,   UpperCAmelCase_							,   atol=1e-3							,   rtol=1e-3      )







"""simple docstring"""


import numpy as np



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_ = 1E-12		,   UpperCAmelCase_ = 1_00		,   )->				tuple[float, np.ndarray]:







       """simple docstring"""
       assert np.shape(UpperCAmelCase_					)[0] == np.shape(UpperCAmelCase_					)[1]
       # Ensure proper dimensionality.
       assert np.shape(UpperCAmelCase_					)[0] == np.shape(UpperCAmelCase_					)[0]
       # Ensure inputs are either both complex or both real
       assert np.iscomplexobj(UpperCAmelCase_					) == np.iscomplexobj(UpperCAmelCase_					)
       UpperCamelCase       			=		np.iscomplexobj(UpperCAmelCase_					)
       if is_complex:
              # Ensure complex input_matrix is Hermitian
              assert np.array_equal(UpperCAmelCase_		,   input_matrix.conj().T					)

       # Set convergence to False. Will define convergence when we exceed max_iterations
       # or when we have small changes from one iteration to next.

       UpperCamelCase       			=		False
       UpperCamelCase       			=		0
       UpperCamelCase       			=		0
       UpperCamelCase       			=		1E12

       while not convergence:
              # Multiple matrix by the vector.
              UpperCamelCase       			=		np.dot(UpperCAmelCase_		,   UpperCAmelCase_					)
              # Normalize the resulting output vector.
              UpperCamelCase       			=		w / np.linalg.norm(UpperCAmelCase_					)
              # Find rayleigh quotient
              # (faster than usual b/c we know vector is normalized already)
              UpperCamelCase       			=		vector.conj().T if is_complex else vector.T
              UpperCamelCase       			=		np.dot(UpperCAmelCase_		,   np.dot(UpperCAmelCase_		,   UpperCAmelCase_					)					)

              # Check convergence.
              UpperCamelCase       			=		np.abs(lambda_ - lambda_previous					) / lambda_
              iterations += 1

              if error <= error_tol or iterations >= max_iterations:
                     UpperCamelCase       			=		True

              UpperCamelCase       			=		lambda_

       if is_complex:
              UpperCamelCase       			=		np.real(lambda_					)

       return lambda_, vector



def 						lowerCamelCase__      (				)->				None:







       """simple docstring"""
       UpperCamelCase       			=		np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]					)
       UpperCamelCase       			=		np.array([41, 4, 20]					)
       UpperCamelCase       			=		real_input_matrix.astype(np.complexaaa					)
       UpperCamelCase       			=		np.triu(1j * complex_input_matrix		,   1					)
       complex_input_matrix += imag_matrix
       complex_input_matrix += -1 * imag_matrix.T
       UpperCamelCase       			=		np.array([41, 4, 20]					).astype(np.complexaaa					)

       for problem_type in ["real", "complex"]:
              if problem_type == "real":
                     UpperCamelCase       			=		real_input_matrix
                     UpperCamelCase       			=		real_vector
              elif problem_type == "complex":
                     UpperCamelCase       			=		complex_input_matrix
                     UpperCamelCase       			=		complex_vector

              # Our implementation.
              UpperCamelCase			,							UpperCamelCase       			=		power_iteration(UpperCAmelCase_		,   UpperCAmelCase_					)

              # Numpy implementation.

              # Get eigenvalues and eigenvectors using built-in numpy
              # eigh (eigh used for symmetric or hermetian matrices).
              UpperCamelCase			,							UpperCamelCase       			=		np.linalg.eigh(UpperCAmelCase_					)
              # Last eigenvalue is the maximum one.
              UpperCamelCase       			=		eigen_values[-1]
              # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
              UpperCamelCase       			=		eigen_vectors[:, -1]

              # Check our implementation and numpy gives close answers.
              assert np.abs(eigen_value - eigen_value_max					) <= 1E-6
              # Take absolute values element wise of each eigenvector.
              # as they are only unique to a minus sign.
              assert np.linalg.norm(np.abs(UpperCAmelCase_					) - np.abs(UpperCAmelCase_					)					) <= 1E-6


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      test_power_iteration()





